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ANALYSING ANALYTICS Gavin Henrick Learning Technology Services
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Learning Analytics

Jul 15, 2015

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Gavin Henrick
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Page 1: Learning Analytics

ANALYSING ANALYTICS Gavin Henrick

Learning Technology Services

Page 3: Learning Analytics

LEARNING ANALYTICS What is it?

Page 4: Learning Analytics

What is Learning Analytics?

“Learning analytics is the measurement, collection, analysis

and reporting of data about learners and their contexts, for

purposes of understanding and optimising learning and the

environments in which it occurs. ” Wikipedia http://en.wikipedia.org/wiki/Learning_analytics

“Field associated with deciphering trends and patterns from

educational big data, or huge sets of student-related data,

to further the advancement of a personalized, supportive

system of higher education” 2013 Horizon Report http://net.educause.edu/ir/library/pdf/HR2013.pdfz

Page 5: Learning Analytics

How?

“Learning analytics is the measurement, collection, analysis

and reporting of data about learners and their contexts, for

purposes of understanding and optimising learning and the

environments in which it occurs. ” Wikipedia http://en.wikipedia.org/wiki/Learning_analytics

“Field associated with deciphering trends and patterns from

educational big data, or huge sets of student-related data,

to further the advancement of a personalized, supportive

system of higher education” 2013 Horizon Report http://net.educause.edu/ir/library/pdf/HR2013.pdfz

Page 6: Learning Analytics

Why?

“Learning analytics is the measurement, collection, analysis

and reporting of data about learners and their contexts, for

purposes of understanding and optimising learning and the

environments in which it occurs. ” Wikipedia http://en.wikipedia.org/wiki/Learning_analytics

“Field associated with deciphering trends and patterns from

educational big data, or huge sets of student-related data,

to further the advancement of a personalized, supportive

system of higher education” 2013 Horizon Report http://net.educause.edu/ir/library/pdf/HR2013.pdfz

Page 7: Learning Analytics

DESCRIPTIVE ANALYTICS

What has happened?

Page 8: Learning Analytics

DIAGNOSTIC ANALYTICS Why this happened?

Page 9: Learning Analytics

PREDICTIVE ANALYTICS What will happen?

Page 10: Learning Analytics

PRESCRIPTIVE ANALYTICS What to do?

Page 11: Learning Analytics

What data is there on students?

Profile Activity

Content Results

Page 12: Learning Analytics

Profile

• Prior skill set

• Prior examination results

• Prior subject choice

• Prior examination levels

• Demographics

Page 13: Learning Analytics

Activity

• Library visits

• Number of books / resources used

• Class attendence

• Wifi access

• Online systems access

Page 14: Learning Analytics

Content

• Which modules

• How many modules

• Level of modules

• Workload of modules

Page 15: Learning Analytics

Results

• Year completion

• Module completion

• Module grades

• Assignment grades

• Question level success

• Surveys

• Competency assessments

• Competency related success

Page 16: Learning Analytics

Key Goals

• Improve student success

• Improve student retention

• Improve the learning experience

Page 17: Learning Analytics

WHO

WHAT

WHERE

WHEN

WHY

Page 18: Learning Analytics

Who are we thinking about?

Consider each of the following questions from the

position of

• A student

• A teacher/lecturer

• A programme /course coordinator

• Student support staff

• Central registry

Page 19: Learning Analytics

Who

Who is going to be using the data or the reports

using the data?

What controls are needed to ensure only those

who should access them get access?

Page 20: Learning Analytics

What

What data and reports are they going to need for

their usage?

Page 21: Learning Analytics

Where

Where do they need these reports and data?

Where and how will they be accessing them

Page 22: Learning Analytics

When

When do they need to get the data, reports

- Different data sources will have different potential

latency

- Different data sets may require different

timeframes for usefulness

- Different data sets may be useful at different

times of year

Page 23: Learning Analytics

Why

Why are they going to use it?

Page 24: Learning Analytics

Useful vs Used

• Lots of data may be useful but not used

• Having reports available to access is no good if

they are not accessed

• Important to identify what will be used and how

Page 25: Learning Analytics

WHY ANALYSE Applying Analytics to learning

Page 26: Learning Analytics

“With this data available

it is wrong to withhold it from the

students themselves”

Page 27: Learning Analytics

What would a student do with the

information he is given through learning

analytics?

Page 28: Learning Analytics

What would a lecturer do with the

information he is given about a student

through learning analytics?

Page 29: Learning Analytics

What would a lecturer do with the

information he is given about his

course through learning analytics?

Page 30: Learning Analytics

SOME QUESTIONS

Page 31: Learning Analytics

Student

• How well am I doing?

• How well am I doing compared to the class?

• How are my friends doing?

• Which subjects should I invest more time in for

greatest benefit?

• What am I not doing that others are doing?

• Is there anything I should be doing that I am not?

Page 32: Learning Analytics

Teacher

• How well are my students doing?

• How well are they doing compared to the class?

• How well are they doing compared to other years?

• Which areas of the curriculum are getting the worst

/ best results?

• Which learning outcomes are not being met?

• Are students using the resources? Which

resources? When ?

• With which resources are students outcomes the

best in assessments?

Page 33: Learning Analytics

Support

• How well are students doing?

• How well are they doing compared to the class?

• How well are they doing compared to other

years?

• Which students are in need of help on a specific

subject?

• Which students are in need of help across many

subjects / in general?

Page 34: Learning Analytics

Admins

• Which courses are students not engaging in?

• Which courses are teachers not engaging in?

• Which courses are students underperforming in?

• Which courses are generating the highest?

• Which students are at risk in a course?

• Which students are at risk in multiple courses?

Page 35: Learning Analytics

ETHICAL CONCERNS Applying Morals to Analytics

Page 36: Learning Analytics

Data and reporting concerns

Some issues for discussion:

• Transparency on data acquisition

• Secure data storage, retention periods

• Ownership of data

• Purpose for reporting on different themes

• Access to different data

Page 37: Learning Analytics

Legal issues

• Data protection laws

• Security policies

• Access policies

• Terms of use

• Student awareness

• Student Impact

Page 38: Learning Analytics

THE OPEN UNIVERSITY An example of transparency in analytics

Page 39: Learning Analytics

The Open University 8 key principles Principle 1: Learning analytics is an ethical practice that should align with core organisational principles, such as open entry to undergraduate level study.

Principle 2: The OU has a responsibility to all stakeholders to use and extract meaning from student data for the benefit of students where feasible.

Principle 3: Students should not be wholly defined by their visible data or our interpretation of that data.

Principle 4: The purpose and the boundaries regarding the use of learning analytics should be well defined and visible.

Principle 5: The University is transparent regarding data collection, and will provide students with the opportunity to update their own data and consent agreements at regular intervals.

Principle 6: Students should be engaged as active agents in the implementation of learning analytics (e.g. informed consent, personalised learning paths, interventions).

Principle 7: Modelling and interventions based on analysis of data should be sound and free from bias.

Principle 8: Adoption of learning analytics within the OU requires broad acceptance of the values and benefits (organisational culture) and the development of appropriate skills across the organisation.

See: http://www.open.ac.uk/students/charter/essential-documents/ethical-use-student-

data-learning-analytics-policy

Page 40: Learning Analytics

Jisc work in the UK

• Code of practice for Learning Analytics – Public

consultation

• http://sclater.com/blog/code-of-practice-for-learning-analytics-

public-consultation/

• Final version of the Code in June

• Interesting breakdown including access, and action responsibilities

Page 41: Learning Analytics

Mobile App. What students want

• http://sclater.com/blog/what-do-students-want-from-a-

learning-analytics-app/

• Some points to consider

• What is analytics to the student

• What do they want tracked

• What is the information that they want access to easily

Page 42: Learning Analytics

References Finding the Prodigal Student: Academics' Analytics at UCD

http://www.heanet.ie/conferences/2014/talks/id/97

Making Sense of Data from your LMS

http://www.heanet.ie/conferences/2014/talks/id/98

Code of practice for learning analytics – A literature review of the ethical and legal issues http://analytics.jiscinvolve.org/wp/2014/12/04/jisc-releases-report-on-ethical-and-legal-challenges-of-learning-analytics/

Learning Analytics – The current state of play in UK Higher and further education http://analytics.jiscinvolve.org/wp/2014/11/20/jisc-releases-new-report-on-learning-analytics-in-the-uk/

Ethical use of Student Data for Learning Analytics Policy – The Open University

http://www.open.ac.uk/students/charter/essential-documents/ethical-use-student-data-learning-analytics-policy